Data visualization

Library

library(tidyverse)

## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.0     ✓ purrr   0.3.4
## ✓ tibble  3.0.1     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.1     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

The mpg data frame

mpg

## # A tibble: 234 x 11
##    manufacturer model    displ  year   cyl trans   drv     cty   hwy fl    class
##    <chr>        <chr>    <dbl> <int> <int> <chr>   <chr> <int> <int> <chr> <chr>
##  1 audi         a4         1.8  1999     4 auto(l… f        18    29 p     comp…
##  2 audi         a4         1.8  1999     4 manual… f        21    29 p     comp…
##  3 audi         a4         2    2008     4 manual… f        20    31 p     comp…
##  4 audi         a4         2    2008     4 auto(a… f        21    30 p     comp…
##  5 audi         a4         2.8  1999     6 auto(l… f        16    26 p     comp…
##  6 audi         a4         2.8  1999     6 manual… f        18    26 p     comp…
##  7 audi         a4         3.1  2008     6 auto(a… f        18    27 p     comp…
##  8 audi         a4 quat…   1.8  1999     4 manual… 4        18    26 p     comp…
##  9 audi         a4 quat…   1.8  1999     4 auto(l… 4        16    25 p     comp…
## 10 audi         a4 quat…   2    2008     4 manual… 4        20    28 p     comp…
## # … with 224 more rows

summary(mpg)

##  manufacturer          model               displ            year     
##  Length:234         Length:234         Min.   :1.600   Min.   :1999  
##  Class :character   Class :character   1st Qu.:2.400   1st Qu.:1999  
##  Mode  :character   Mode  :character   Median :3.300   Median :2004  
##                                        Mean   :3.472   Mean   :2004  
##                                        3rd Qu.:4.600   3rd Qu.:2008  
##                                        Max.   :7.000   Max.   :2008  
##       cyl           trans               drv                 cty       
##  Min.   :4.000   Length:234         Length:234         Min.   : 9.00  
##  1st Qu.:4.000   Class :character   Class :character   1st Qu.:14.00  
##  Median :6.000   Mode  :character   Mode  :character   Median :17.00  
##  Mean   :5.889                                         Mean   :16.86  
##  3rd Qu.:8.000                                         3rd Qu.:19.00  
##  Max.   :8.000                                         Max.   :35.00  
##       hwy             fl               class          
##  Min.   :12.00   Length:234         Length:234        
##  1st Qu.:18.00   Class :character   Class :character  
##  Median :24.00   Mode  :character   Mode  :character  
##  Mean   :23.44                                        
##  3rd Qu.:27.00                                        
##  Max.   :44.00

Creating a ggplot

ggplot(data=mpg)+
  geom_point(mapping=aes(x=displ, y=hwy))

• ggplot(): create a coordinate system that you can add layers to
• geom_point(): add a layer of points to the plot
• Each geom function in ggplot2 takes a mapping argument, which defines how variables in the dataset are mapped to visual properties.

A graphing template

ggplot(date=<DATA>)+<GEROM_FUNCTION>(mapping=aes(<MAPPINGS>))

Aesthetic mappings

• To map an aesthetic to a variable, associate the name of the aesthetic to the name of the variable inside aes(). ggplot2 will automatically assign a unique level of the aesthetic to each unique value of the variable, a process known as scaling. ggplot2 will also add a legend that explains which levels correspond to which values.

• color

ggplot(data=mpg)+geom_point(map=aes(x=displ, y=hwy, color=class))

• alpha: control the transparency

ggplot(data=mpg)+geom_point(map=aes(x=displ, y=hwy, alpha=class))

## Warning: Using alpha for a discrete variable is not advised.

• shape: control the shape

ggplot(data=mpg)+geom_point(mapping=aes(x=displ, y=hwy, shape=class))

## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.

## Warning: Removed 62 rows containing missing values (geom_point).

• We can also set the aesthetic properties of the geom manually. For example,

ggplot(data=mpg)+geom_point(map=aes(x=displ, y=hwy), color="blue")

• stroke: control the thickness of point boundary

ggplot(data=mpg)+geom_point(mapping=aes(x=displ, y=hwy), size=5, color="white", stroke=1)

ggplot(data=mpg)+geom_point(mapping=aes(x=displ, y=hwy), size=5, color="white", stroke=2)

• Common probnlem: the + sign has to come at the end of the line, not the start.

Facets

• One way to add additional variables is with aesthetics. Another way, particularly useful for categprical variables, is to split your plot into facets, subplots that each display one subset of the data.

• To facet your plot by a single variable, use facet_wrap(). The first argument of facet_wrap() should be a formula, which you create with ~ followed by a variable name. The variabe that you pass to facet_wrap()should be discrete.

ggplot(data=mpg)+
  geom_point(mapping=aes(x=displ, y=hwy))+
  facet_wrap(~class, nrow=2)

• To facet your plot on the combination of two variables, add facet_grid() to your plot call. The first argument of facet_grid() is also a formula. This time the formula should contain two variable names separated by a ~.

ggplot(data=mpg)+
  geom_point(mapping=aes(x=displ, y=hwy))+
  facet_grid(drv~cyl)

• If you prefer to not facet in the rows or columns dimension, use . instead of a variable name.

ggplot(data=mpg)+
  geom_point(mapping=aes(x=displ, y=hwy))+
  facet_grid(.~cyl)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(drv ~ .)

Geometric objects

• A geom is the geometrical object that a plot uses to represent data.

gg=ggplot(data=mpg)

gg+geom_point(mapping=aes(x=displ, y=hwy))

gg+geom_smooth(mapping=aes(x=displ, y=hwy))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

gg+geom_smooth(mapping=aes(x=displ, y=hwy, linetype=drv))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

gg+
  geom_point(mapping=aes(x=displ, y=hwy, color=drv))+
  geom_smooth(mapping=aes(x=displ, y=hwy, linetype=drv))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data=mpg, mapping=aes(x=displ, y=hwy))+
  geom_point()+
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

gg+geom_smooth(mapping=aes(x=displ, y=hwy, group=drv))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

gg+geom_smooth(mapping=aes(x=displ, y=hwy, color=drv), show.legend=FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data=mpg, mapping=aes(x=displ, y=hwy))+
  geom_point(mapping=aes(color=class))+
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data=mpg, mapping=aes(x=displ, y=hwy))+
  geom_point(mapping=aes(color=class))+
  geom_smooth(data=filter(mpg, class=="subcompact"), se=FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point() + 
  geom_smooth(se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

• Exercise 6

ggplot(data=mpg, mapping=aes(x=displ, y=hwy))+
  geom_point()+
  geom_smooth(color="blue", se=FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data=mpg, mapping=aes(x=displ, y=hwy))+
  geom_point()+
  geom_smooth(aes(group=drv), se=FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data=mpg, mapping=aes(x=displ, y=hwy, color=drv))+
  geom_point()+
  geom_smooth(se=FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data=mpg )+
  geom_point(mapping=aes(x=displ, y=hwy, color=drv))+
  geom_smooth(mapping=aes(x=displ, y=hwy), se=FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data=mpg, mapping=aes(x=displ, y=hwy))+
  geom_point(mapping=aes(color=drv))+
  geom_smooth(aes(linetype=drv), se=FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data=mpg)+
  geom_point(mapping=aes(x=displ, y=hwy, color=drv), stroke=2 )

Statistical transformations

• bar chart

diamonds

## # A tibble: 53,940 x 10
##    carat cut       color clarity depth table price     x     y     z
##    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1 0.23  Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
##  2 0.21  Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
##  3 0.23  Good      E     VS1      56.9    65   327  4.05  4.07  2.31
##  4 0.290 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
##  5 0.31  Good      J     SI2      63.3    58   335  4.34  4.35  2.75
##  6 0.24  Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
##  7 0.24  Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
##  8 0.26  Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
##  9 0.22  Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
## 10 0.23  Very Good H     VS1      59.4    61   338  4     4.05  2.39
## # … with 53,930 more rows

ggplot(data=diamonds)+
  geom_bar(mapping=aes(x=cut))

• You can recreate the previous plot using stat_count() instead of geom_bar():

ggplot(data=diamonds)+
  stat_count(mapping=aes(x=cut))